Trends in High-Acuity Cardiovascular Events During the COVID-19 Pandemic

This cohort study describes changes in myocardial infarction and stroke hospitalizations as well as congestive heart failure, angina, and transient ischemic attack incidents months before and after March 2020 among insured people in New England.


eMethods A. Creation of high-acuity cardiovascular event episodes
We created high-acuity cardiovascular event episodes by generating 10-day windows (beginning with the day of a high-acuity cardiovascular event visit to the outpatient or high-acuity setting) during which we captured visits that had the same cluster of relevant diagnoses (e.g., all stroke diagnoses; eTable 1).This approach prevents "double counting" of situations such as a 3-day hospitalization for myocardial infarction followed by a related emergency department visit 2 days later.We required that the diagnosis of interest be in the primary, secondary, or tertiary position.For myocardial infarction and stroke, we only included 10-day episodes if they included a hospitalization for the relevant diagnosis, a standard approach with high specificity. 1,2For heart failure, angina, and transient ischemic attack, we included 10-day episodes if there was an emergency department, observation unit, or hospital diagnosis because these represent higher acuity presentations in contrast to outpatient-based care that might represent routine monitoring or follow-up of a preexisting condition or episode.Our measure of interest was the number of high-acuity episodes per patient per month presenting to the health system.

C. Sample sizes and enrollment duration
Sample sizes ranged from 283,467 to 376,511 per month over the study period.Mean and median enrollment durations were 28.4 months and 24.0 months, respectively.

D. Statistical analysis
We hypothesized that over the follow up period, monthly points would initially be statistically below the trend predicted by on our 3-year baseline, and then be statistically above predicted in later months of the study period.Our data were organized at the person-month level, and subjects appeared only in months in which they were enrolled.Month rows in our analytic dataset were labelled 1 (March 2017, start of study/ baseline) to 58 (December 2021, end of study).
The cardiovascular measures of interest comprised the dependent variables in our statistical models, and the independent variables were age group, male/female, trend, level change, trend change, and change in trend change (i.e., trend change*trend change, a quadratic term).The trend term starts at 1 for the first month of the baseline and counts up to the last month (i.e., 58 in this case).The level change term is zero for all baseline months (i.e., months 1 to 36 in this case) and 1 for all follow up months (i.e., 38 to 58 in this case).The trend change term is zero for all baseline months then begins counting up from 1 (for the first follow up month) to the number of follow up months (i.e., 21 in this case).The quadratic term is the square of this trend change term.
We used related modeling approaches to (1) generate statistical estimates of absolute and relative differences versus expected at each month and (2) create unadjusted plots of predicted and fitted trends.

Statistical estimates of absolute and relative differences versus expected at each month
We first ran a linear segmented regression model with trend, level change, trend change, and trend change*trend change terms, adjusted for age category and sex, while accounting for person-level clustering.This was a least square regression without fixed effects and with standard errors robust to heteroskedasticity and within-cluster correlation.We specified the member ID as the clustering variable.This model produced an expected trend from April 2020 to December 2021 based on the March 2017 to February 2020 pre-COVID trend.It also fit a modeled trend to the follow-up points.Using this model and its parameter estimates, we applied nonlinear combinations of parameters to calculate each adjusted follow up point's absolute and relative differences (and 95% confidence intervals) compared to the value predicted by the extension of the baseline linear trend.

Plot unadjusted predicted fitted trends
Because nonlinear combinations of parameters are unable to simultaneously generate adjusted parameter estimates and accurate fitted linear trends, we used a separate unadjusted model to create fitted and predicted trends for plotting and visualization.We used a linear segmented regression model with generalized estimating equations and we included trend, level change, trend change, and trend change*trend change terms, while accounting for person-level clustering.This model used a sandwich estimator for the standard errors.

Cardiovascular event International Classification of Diseases, 10 th Edition, Codes
2ased on 2014-2018 American Community Survey at the census tract level.2Denominatorforstate-levelcharacteristics is the overall state population rather than the age ≥35 years state population.3Definedas>66% of members in the census tract identifying as the given race/ethnicity category.4Definedas>10% of households in the census tract living below the federal poverty level. 5 households in the neighborhood with high school or less education level.5Peoplewithmissingstate of residence were included in analyses.Based on 2014-2018 American Community Survey at the census tract level.2Denominatorforstate-levelcharacteristics is the overall state population rather than the age ≥35 years state population.3nedas>66% of members in the census tract identifying as the given race/ethnicity category.4Definedas>10% of households in the census tract living below the federal poverty level.5 Defined as >35% of households in the neighborhood with high school or less education level.5Peoplewithmissingstate of residence were included in analyses.Population characteristics in March 2017, 2020, and 2021 of study members from Maine, and analogous characteristics of the overall Maine population, based on the 2014-2018 American Community Survey at the census tract level Based on 2014-2018 American Community Survey at the census tract level.2Denominatorforstate-levelcharacteristics is the overall state population rather than the age ≥35 years state population. 3as >66% of members in the census tract identifying as the given race/ethnicity category.4Definedas>10% of households in the census tract living below the federal poverty level.5 Defined as >35% of households in the neighborhood with high school or less education level.5Peoplewithmissingstate of residence were included in analyses.Based on 2014-2018 American Community Survey at the census tract level.2Denominatorforstate-levelcharacteristics is the overall state population rather than the age ≥35 years state population.3Definedas >66% of members in the census tract identifying as the given race/ethnicity category. 4fined as >10% of households in the census tract living below the federal poverty level. 5fined as >35% of households in the neighborhood with high school or less education level.5Peoplewith missing state of residence were included in analyses.Billing codes used to identify high-acuity cardiovascular events 2Based on 2014-2018 American Community Survey at the census tract level.2Definedas>66% of members in the census tract identifying as the given race/ethnicity category.3Definedas >10% of households in the census tract living below the federal poverty level.4  fined as >35% of households in the neighborhood with high school or less education level.-5 People with missing state of residence were included in analyses.© 2024 Wharam JF et al. JMA Health Forum. eable 4. eTable 6.